Table REF  shows the micro F1 scores on in-domain AIDA-B dataset of the SOTA methods and ours, which all use Wikipedia and YAGO mention-entity index. The models are divided into two groups: local models and local & global models. As we can see, our proposed model, BERT-Entity-Sim, outperforms all previous methods. Our local model achieves a 1.31 improvement in terms of F1 over its corresponding baseline [1]}, yielding a very competitive local model with an average 90.06 F1 score even surpassing the performance of four local & global models. Equipped with the global modeling method of [1]} ganea2017deep, the performance of our model further increase to 93.54 with an average 1.32 improvement in terms of F1 over [1]} ganea2017deep. In addition, our method outperforms [4]} le-titov-2018-improving model by 0.47 point.
The model of [4]} le-titov-2018-improving is a multi-relational extension of [1]} ganea2017deep's global modeling method while keeps exactly the same local context model.
Our better local context model should be orthogonal with them and has potential more applications on short texts (e.g. tweets) where global modeling has little benefits.
Moreover, BERT+G&H’s embeddings performs significantly worse than the baseline [1]} and our proposed BERT-Entity-Sim model. The reason is that BERT-based context representation space and [1]}’s entity embeddings space are heterogeneous. [1]}’s entity embeddings are bootstrapped from word embeddings which mainly capture topic level entity relatedness, while BERT-based context representation is derived from BERT which naturally captures type information. The non-parallel information in both context and entity sides makes it difficult to learn the alignment parameter \({\mathbf {W}}\)  and results in the poor generalization performance.
